Overview

Dataset statistics

Number of variables24
Number of observations917
Missing cells2751
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory172.1 KiB
Average record size in memory192.1 B

Variable types

Numeric6
Categorical15
Unsupported3

Alerts

filetype has constant value "Win64 DLL" Constant
codesize has constant value "32768" Constant
timestamp has constant value "2020" Constant
resources_len has constant value "1" Constant
imp_hash has constant value "fbcff5951ad0c204f4744c629548c6c6" Constant
ssdeep_blocksize has constant value "24576" Constant
filename has a high cardinality: 383 distinct values High cardinality
authentihash has a high cardinality: 872 distinct values High cardinality
file_md5 has a high cardinality: 872 distinct values High cardinality
sha1 has a high cardinality: 872 distinct values High cardinality
sha256 has a high cardinality: 872 distinct values High cardinality
ssdeep_hash1 has a high cardinality: 194 distinct values High cardinality
ssdeep_hash2 has a high cardinality: 140 distinct values High cardinality
tlsh has a high cardinality: 786 distinct values High cardinality
vhash has a high cardinality: 612 distinct values High cardinality
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with df_index and 1 other fieldsHigh correlation
win_count is highly correlated with df_index and 1 other fieldsHigh correlation
malicious is highly correlated with undetectedHigh correlation
undetected is highly correlated with maliciousHigh correlation
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with df_index and 1 other fieldsHigh correlation
win_count is highly correlated with df_index and 1 other fieldsHigh correlation
malicious is highly correlated with undetectedHigh correlation
undetected is highly correlated with maliciousHigh correlation
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with df_index and 1 other fieldsHigh correlation
win_count is highly correlated with df_index and 1 other fieldsHigh correlation
malicious is highly correlated with undetectedHigh correlation
undetected is highly correlated with maliciousHigh correlation
filetype is highly correlated with imp_hash and 4 other fieldsHigh correlation
imp_hash is highly correlated with filetype and 4 other fieldsHigh correlation
ssdeep_blocksize is highly correlated with filetype and 4 other fieldsHigh correlation
timestamp is highly correlated with filetype and 4 other fieldsHigh correlation
resources_len is highly correlated with filetype and 4 other fieldsHigh correlation
codesize is highly correlated with filetype and 4 other fieldsHigh correlation
df_index is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with df_index and 1 other fieldsHigh correlation
win_count is highly correlated with df_index and 1 other fieldsHigh correlation
malicious is highly correlated with undetectedHigh correlation
undetected is highly correlated with maliciousHigh correlation
icon_dhash has 917 (100.0%) missing values Missing
icon_raw_md5 has 917 (100.0%) missing values Missing
header_hash has 917 (100.0%) missing values Missing
authentihash is uniformly distributed Uniform
file_md5 is uniformly distributed Uniform
sha1 is uniformly distributed Uniform
sha256 is uniformly distributed Uniform
tlsh is uniformly distributed Uniform
vhash is uniformly distributed Uniform
df_index has unique values Unique
Unnamed: 0 has unique values Unique
win_count has unique values Unique
icon_dhash is an unsupported type, check if it needs cleaning or further analysis Unsupported
icon_raw_md5 is an unsupported type, check if it needs cleaning or further analysis Unsupported
header_hash is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-05 01:53:22.320449
Analysis finished2022-09-05 01:53:28.880622
Duration6.56 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct917
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean519076.2694
Minimum990
Maximum1036963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2022-09-05T11:53:28.957186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile125633.4
Q1191275
median379481
Q3898675
95-th percentile945258
Maximum1036963
Range1035973
Interquartile range (IQR)707400

Descriptive statistics

Standard deviation332867.0401
Coefficient of variation (CV)0.6412680751
Kurtosis-1.745247086
Mean519076.2694
Median Absolute Deviation (MAD)230752
Skewness0.147608515
Sum475992939
Variance1.108004664 × 1011
MonotonicityStrictly increasing
2022-09-05T11:53:29.065644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9901
 
0.1%
8671171
 
0.1%
8672241
 
0.1%
8675991
 
0.1%
8678201
 
0.1%
8680391
 
0.1%
8680611
 
0.1%
8683461
 
0.1%
8690481
 
0.1%
8691021
 
0.1%
Other values (907)907
98.9%
ValueCountFrequency (%)
9901
0.1%
16071
0.1%
20171
0.1%
22171
0.1%
64591
0.1%
218561
0.1%
265831
0.1%
403131
0.1%
451681
0.1%
452111
0.1%
ValueCountFrequency (%)
10369631
0.1%
10328161
0.1%
10211461
0.1%
9888471
0.1%
9825711
0.1%
9734271
0.1%
9708461
0.1%
9695001
0.1%
9645011
0.1%
9642491
0.1%

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct917
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean519076.2694
Minimum990
Maximum1036963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2022-09-05T11:53:29.166891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile125633.4
Q1191275
median379481
Q3898675
95-th percentile945258
Maximum1036963
Range1035973
Interquartile range (IQR)707400

Descriptive statistics

Standard deviation332867.0401
Coefficient of variation (CV)0.6412680751
Kurtosis-1.745247086
Mean519076.2694
Median Absolute Deviation (MAD)230752
Skewness0.147608515
Sum475992939
Variance1.108004664 × 1011
MonotonicityStrictly increasing
2022-09-05T11:53:29.265247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9901
 
0.1%
8671171
 
0.1%
8672241
 
0.1%
8675991
 
0.1%
8678201
 
0.1%
8680391
 
0.1%
8680611
 
0.1%
8683461
 
0.1%
8690481
 
0.1%
8691021
 
0.1%
Other values (907)907
98.9%
ValueCountFrequency (%)
9901
0.1%
16071
0.1%
20171
0.1%
22171
0.1%
64591
0.1%
218561
0.1%
265831
0.1%
403131
0.1%
451681
0.1%
452111
0.1%
ValueCountFrequency (%)
10369631
0.1%
10328161
0.1%
10211461
0.1%
9888471
0.1%
9825711
0.1%
9734271
0.1%
9708461
0.1%
9695001
0.1%
9645011
0.1%
9642491
0.1%

filename
Categorical

HIGH CARDINALITY

Distinct383
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2022041900/2022041900_47
 
17
2022041900/2022041900_40
 
11
2022041900/2022041900_11
 
11
2022041900/2022041900_32
 
10
2022041900/2022041900_12
 
10
Other values (378)
858 

Length

Max length33
Median length24
Mean length24.37513631
Min length23

Characters and Unicode

Total characters22352
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique193 ?
Unique (%)21.0%

Sample

1st row20220329/2022032900/2022032900_10
2nd row20220329/2022032900/2022032900_11
3rd row20220329/2022032900/2022032900_11
4th row20220329/2022032900/2022032900_12
5th row20220329/2022032900/2022032900_22

Common Values

ValueCountFrequency (%)
2022041900/2022041900_4717
 
1.9%
2022041900/2022041900_4011
 
1.2%
2022041900/2022041900_1111
 
1.2%
2022041900/2022041900_3210
 
1.1%
2022041900/2022041900_1210
 
1.1%
2022041900/2022041900_5610
 
1.1%
2022041922/2022041922_19
 
1.0%
2022041921/2022041921_529
 
1.0%
2022041900/2022041900_559
 
1.0%
2022041901/2022041901_19
 
1.0%
Other values (373)812
88.5%

Length

2022-09-05T11:53:29.346571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022041900/2022041900_4717
 
1.9%
2022041900/2022041900_1111
 
1.2%
2022041900/2022041900_4011
 
1.2%
2022041900/2022041900_3210
 
1.1%
2022041900/2022041900_1210
 
1.1%
2022041900/2022041900_5610
 
1.1%
2022041900/2022041900_559
 
1.0%
2022041901/2022041901_19
 
1.0%
2022041921/2022041921_529
 
1.0%
2022041922/2022041922_19
 
1.0%
Other values (373)812
88.5%

Most occurring characters

ValueCountFrequency (%)
27041
31.5%
05530
24.7%
12626
 
11.7%
42007
 
9.0%
91989
 
8.9%
/976
 
4.4%
_917
 
4.1%
3546
 
2.4%
5311
 
1.4%
7175
 
0.8%
Other values (2)234
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20459
91.5%
Other Punctuation976
 
4.4%
Connector Punctuation917
 
4.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
27041
34.4%
05530
27.0%
12626
 
12.8%
42007
 
9.8%
91989
 
9.7%
3546
 
2.7%
5311
 
1.5%
7175
 
0.9%
6126
 
0.6%
8108
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/976
100.0%
Connector Punctuation
ValueCountFrequency (%)
_917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common22352
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
27041
31.5%
05530
24.7%
12626
 
11.7%
42007
 
9.0%
91989
 
8.9%
/976
 
4.4%
_917
 
4.1%
3546
 
2.4%
5311
 
1.4%
7175
 
0.8%
Other values (2)234
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27041
31.5%
05530
24.7%
12626
 
11.7%
42007
 
9.0%
91989
 
8.9%
/976
 
4.4%
_917
 
4.1%
3546
 
2.4%
5311
 
1.4%
7175
 
0.8%
Other values (2)234
 
1.0%

win_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct917
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301964.446
Minimum991
Maximum591567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2022-09-05T11:53:29.423651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum991
5-th percentile95029.4
Q1178747
median278964
Q3453279
95-th percentile499862
Maximum591567
Range590576
Interquartile range (IQR)274532

Descriptive statistics

Standard deviation143077.2092
Coefficient of variation (CV)0.4738213756
Kurtosis-1.283022709
Mean301964.446
Median Absolute Deviation (MAD)110051
Skewness-0.01648462829
Sum276901397
Variance2.047108779 × 1010
MonotonicityNot monotonic
2022-09-05T11:53:29.513532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9911
 
0.1%
4217211
 
0.1%
4218281
 
0.1%
4222031
 
0.1%
4224241
 
0.1%
4226431
 
0.1%
4226651
 
0.1%
4229501
 
0.1%
4236521
 
0.1%
4237061
 
0.1%
Other values (907)907
98.9%
ValueCountFrequency (%)
9911
0.1%
16081
0.1%
20181
0.1%
22181
0.1%
38961
0.1%
42221
0.1%
42321
0.1%
50001
0.1%
52681
0.1%
54811
0.1%
ValueCountFrequency (%)
5915671
0.1%
5874201
0.1%
5757501
0.1%
5434511
0.1%
5371751
0.1%
5280311
0.1%
5254501
0.1%
5241041
0.1%
5191051
0.1%
5188531
0.1%

authentihash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct872
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
c657dd2a2133cf2df538a1061c47a40a1ed26d1768a6344af2fe54a5da222c5b
 
3
32fa4c1fbc826879408746ffb81874bac8170c6db1e305f216c2b8b793b3eda0
 
2
bf5dc2459e36c208900281538622f087bc21dff9558b3f5bf0305ce3ade3cb38
 
2
4af1b7824cb41896b8f3a7e1a886d849b5c404f527c2327720c0fede0eb948bc
 
2
5044f32509547bb07db86bfbd555963d9a4261bcc4567060d537d6db0701b89e
 
2
Other values (867)
906 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters58688
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique828 ?
Unique (%)90.3%

Sample

1st row32fa4c1fbc826879408746ffb81874bac8170c6db1e305f216c2b8b793b3eda0
2nd row208f9cdedc66cb3f91e2d654f0f5a1f7957434bb919f76e53ad95095532b7d7d
3rd row6c5850bae4d1db26a9ee302f4367a35470000268bdfd797d966e4af46ed18305
4th rowb34a211be3171766ed4a6839a8ce7c213faf1ea8216dfa5b9ba905684a6963cd
5th row83faacda9de3a17fda6b155dab20fb20011dd6862a33b5d4c9b12f5a3c330f3b

Common Values

ValueCountFrequency (%)
c657dd2a2133cf2df538a1061c47a40a1ed26d1768a6344af2fe54a5da222c5b3
 
0.3%
32fa4c1fbc826879408746ffb81874bac8170c6db1e305f216c2b8b793b3eda02
 
0.2%
bf5dc2459e36c208900281538622f087bc21dff9558b3f5bf0305ce3ade3cb382
 
0.2%
4af1b7824cb41896b8f3a7e1a886d849b5c404f527c2327720c0fede0eb948bc2
 
0.2%
5044f32509547bb07db86bfbd555963d9a4261bcc4567060d537d6db0701b89e2
 
0.2%
37633a6d5b40f2784ad0635b21be969f71ae816a816b15d1f8fcb0362b3863d62
 
0.2%
5775d6bd5190879cc5f62e02a37fa563581727978640d4be88f75096c217d57f2
 
0.2%
114a51851b218d5c8fe620722c45c1827657e76c5e0408aadc1a9a9e1ecad03c2
 
0.2%
28612e1345663ca86dd9e1989e2164e8eac9d923ac53b5c864e5f9d0eecc71852
 
0.2%
85e0aee89052e994d135c64a45cc76b576bb3dc2793f90a3bc7f7c18e371e92a2
 
0.2%
Other values (862)896
97.7%

Length

2022-09-05T11:53:29.596510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c657dd2a2133cf2df538a1061c47a40a1ed26d1768a6344af2fe54a5da222c5b3
 
0.3%
62f5df9a4c87a654319f3d5ce5eab0a95a479b5a7186d3a2abafdbe8438bd8bc2
 
0.2%
07a8d3bb8669e68987e8a4087c8be91069a0221d306a696d80afa0665122e74f2
 
0.2%
3b6596815664f4b7fd10af41605f35d7f0ac796159d6b411410bdc81e0d798772
 
0.2%
5de3f26dcf61e4a426b9579ddff13649f19cded7877706f554b09320bf5cb0872
 
0.2%
fff4704e2ef9930d8dbd7fe3fb020ce217d20ec42c0c681083bdf93edb0070762
 
0.2%
6c0dbbe3f26fffbf63bd51a5e5926e58dcbffa036deed234c93fa5ed4368e8f92
 
0.2%
208f9cdedc66cb3f91e2d654f0f5a1f7957434bb919f76e53ad95095532b7d7d2
 
0.2%
e210d7fa17c272c5772cbd1e3442aaf8db84f52633f3be2ed07a004426cc93162
 
0.2%
c518190e176fe5cce30f2106f600827e0c7193c2b99bf0a671dd91e241428a3e2
 
0.2%
Other values (862)896
97.7%

Most occurring characters

ValueCountFrequency (%)
d3803
 
6.5%
93779
 
6.4%
83728
 
6.4%
e3718
 
6.3%
33692
 
6.3%
f3680
 
6.3%
b3680
 
6.3%
23675
 
6.3%
73671
 
6.3%
c3653
 
6.2%
Other values (6)21609
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36547
62.3%
Lowercase Letter22141
37.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
93779
10.3%
83728
10.2%
33692
10.1%
23675
10.1%
73671
10.0%
13627
9.9%
53621
9.9%
03620
9.9%
63610
9.9%
43524
9.6%
Lowercase Letter
ValueCountFrequency (%)
d3803
17.2%
e3718
16.8%
f3680
16.6%
b3680
16.6%
c3653
16.5%
a3607
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common36547
62.3%
Latin22141
37.7%

Most frequent character per script

Common
ValueCountFrequency (%)
93779
10.3%
83728
10.2%
33692
10.1%
23675
10.1%
73671
10.0%
13627
9.9%
53621
9.9%
03620
9.9%
63610
9.9%
43524
9.6%
Latin
ValueCountFrequency (%)
d3803
17.2%
e3718
16.8%
f3680
16.6%
b3680
16.6%
c3653
16.5%
a3607
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII58688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d3803
 
6.5%
93779
 
6.4%
83728
 
6.4%
e3718
 
6.3%
33692
 
6.3%
f3680
 
6.3%
b3680
 
6.3%
23675
 
6.3%
73671
 
6.3%
c3653
 
6.2%
Other values (6)21609
36.8%

filetype
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Win64 DLL
917 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters8253
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWin64 DLL
2nd rowWin64 DLL
3rd rowWin64 DLL
4th rowWin64 DLL
5th rowWin64 DLL

Common Values

ValueCountFrequency (%)
Win64 DLL917
100.0%

Length

2022-09-05T11:53:29.658780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T11:53:29.737051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
win64917
50.0%
dll917
50.0%

Most occurring characters

ValueCountFrequency (%)
L1834
22.2%
W917
11.1%
i917
11.1%
n917
11.1%
6917
11.1%
4917
11.1%
917
11.1%
D917
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3668
44.4%
Lowercase Letter1834
22.2%
Decimal Number1834
22.2%
Space Separator917
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L1834
50.0%
W917
25.0%
D917
25.0%
Lowercase Letter
ValueCountFrequency (%)
i917
50.0%
n917
50.0%
Decimal Number
ValueCountFrequency (%)
6917
50.0%
4917
50.0%
Space Separator
ValueCountFrequency (%)
917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5502
66.7%
Common2751
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
L1834
33.3%
W917
16.7%
i917
16.7%
n917
16.7%
D917
16.7%
Common
ValueCountFrequency (%)
6917
33.3%
4917
33.3%
917
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L1834
22.2%
W917
11.1%
i917
11.1%
n917
11.1%
6917
11.1%
4917
11.1%
917
11.1%
D917
11.1%

codesize
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
32768
917 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4585
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row32768
2nd row32768
3rd row32768
4th row32768
5th row32768

Common Values

ValueCountFrequency (%)
32768917
100.0%

Length

2022-09-05T11:53:29.789046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T11:53:29.850947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
32768917
100.0%

Most occurring characters

ValueCountFrequency (%)
3917
20.0%
2917
20.0%
7917
20.0%
6917
20.0%
8917
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4585
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3917
20.0%
2917
20.0%
7917
20.0%
6917
20.0%
8917
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common4585
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3917
20.0%
2917
20.0%
7917
20.0%
6917
20.0%
8917
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3917
20.0%
2917
20.0%
7917
20.0%
6917
20.0%
8917
20.0%

timestamp
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2020
917 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3668
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020917
100.0%

Length

2022-09-05T11:53:29.901791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T11:53:29.964673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2020917
100.0%

Most occurring characters

ValueCountFrequency (%)
21834
50.0%
01834
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3668
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21834
50.0%
01834
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common3668
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
21834
50.0%
01834
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21834
50.0%
01834
50.0%

malicious
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.59323882
Minimum27
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2022-09-05T11:53:30.018188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile45
Q148
median50
Q351
95-th percentile53
Maximum56
Range29
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.956983829
Coefficient of variation (CV)0.05962473714
Kurtosis10.06119141
Mean49.59323882
Median Absolute Deviation (MAD)2
Skewness-2.076608758
Sum45477
Variance8.743753363
MonotonicityNot monotonic
2022-09-05T11:53:30.086861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
50157
17.1%
51151
16.5%
49147
16.0%
52121
13.2%
4897
10.6%
5364
7.0%
4751
 
5.6%
4633
 
3.6%
5425
 
2.7%
4517
 
1.9%
Other values (13)54
 
5.9%
ValueCountFrequency (%)
271
 
0.1%
281
 
0.1%
301
 
0.1%
362
 
0.2%
381
 
0.1%
394
 
0.4%
404
 
0.4%
413
 
0.3%
425
0.5%
4311
1.2%
ValueCountFrequency (%)
562
 
0.2%
558
 
0.9%
5425
 
2.7%
5364
7.0%
52121
13.2%
51151
16.5%
50157
17.1%
49147
16.0%
4897
10.6%
4751
 
5.6%

undetected
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.98037077
Minimum13
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2022-09-05T11:53:30.155058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q117
median18
Q319
95-th percentile21
Maximum23
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.746376996
Coefficient of variation (CV)0.0971268623
Kurtosis0.02252998509
Mean17.98037077
Median Absolute Deviation (MAD)1
Skewness-0.167103886
Sum16488
Variance3.049832613
MonotonicityNot monotonic
2022-09-05T11:53:30.215961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18223
24.3%
19191
20.8%
17157
17.1%
20108
11.8%
16100
10.9%
1553
 
5.8%
2146
 
5.0%
1418
 
2.0%
2210
 
1.1%
137
 
0.8%
ValueCountFrequency (%)
137
 
0.8%
1418
 
2.0%
1553
 
5.8%
16100
10.9%
17157
17.1%
18223
24.3%
19191
20.8%
20108
11.8%
2146
 
5.0%
2210
 
1.1%
ValueCountFrequency (%)
234
 
0.4%
2210
 
1.1%
2146
 
5.0%
20108
11.8%
19191
20.8%
18223
24.3%
17157
17.1%
16100
10.9%
1553
 
5.8%
1418
 
2.0%

resources_len
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1
917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters917
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1917
100.0%

Length

2022-09-05T11:53:30.279588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T11:53:30.339458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1917
100.0%

Most occurring characters

ValueCountFrequency (%)
1917
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number917
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common917
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1917
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1917
100.0%

sections_len
Real number (ℝ≥0)

Distinct30
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.46673937
Minimum13
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2022-09-05T11:53:30.396698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile39.8
Q150
median50
Q350
95-th percentile50
Maximum50
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.841847642
Coefficient of variation (CV)0.09990042049
Kurtosis19.24484922
Mean48.46673937
Median Absolute Deviation (MAD)0
Skewness-4.208321989
Sum44444
Variance23.44348859
MonotonicityNot monotonic
2022-09-05T11:53:30.476295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
50765
83.4%
4721
 
2.3%
4818
 
2.0%
4514
 
1.5%
4613
 
1.4%
4312
 
1.3%
4411
 
1.2%
426
 
0.7%
376
 
0.7%
356
 
0.7%
Other values (20)45
 
4.9%
ValueCountFrequency (%)
131
 
0.1%
193
0.3%
204
0.4%
212
0.2%
221
 
0.1%
231
 
0.1%
252
0.2%
272
0.2%
281
 
0.1%
292
0.2%
ValueCountFrequency (%)
50765
83.4%
495
 
0.5%
4818
 
2.0%
4721
 
2.3%
4613
 
1.4%
4514
 
1.5%
4411
 
1.2%
4312
 
1.3%
426
 
0.7%
415
 
0.5%

file_md5
Categorical

HIGH CARDINALITY
UNIFORM

Distinct872
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
4b4c86299067f5306398db1e988efcf0
 
3
a5aca04c37505d443e527b377c1f5355
 
2
bd65f2c03d7aaf3575c92c9488aba9e2
 
2
08e9ddcba10a559604b2fa971804cfee
 
2
5b123a99ed09a95dc4603622bf14ea0d
 
2
Other values (867)
906 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters29344
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique828 ?
Unique (%)90.3%

Sample

1st rowa5aca04c37505d443e527b377c1f5355
2nd rowb4f0a3935f7048d2bec399d58ba12293
3rd row6a72ebffbfae3512b4d700614d42eb58
4th row6274ed7e9bddf3ab9d1597303d350491
5th row1926f7be52a0897fe0c3179458b73879

Common Values

ValueCountFrequency (%)
4b4c86299067f5306398db1e988efcf03
 
0.3%
a5aca04c37505d443e527b377c1f53552
 
0.2%
bd65f2c03d7aaf3575c92c9488aba9e22
 
0.2%
08e9ddcba10a559604b2fa971804cfee2
 
0.2%
5b123a99ed09a95dc4603622bf14ea0d2
 
0.2%
8b3beab45040d5bd09094e7dd70920ba2
 
0.2%
2fda95c4c1b10bc874ec78620e10bb912
 
0.2%
e91a7ce39e80e6ca932efebf6266ef902
 
0.2%
dbd6544ed574fc5ca920644bbb80bef82
 
0.2%
dd566b2b811fa6cfc9f85ddf06208f542
 
0.2%
Other values (862)896
97.7%

Length

2022-09-05T11:53:30.552715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4b4c86299067f5306398db1e988efcf03
 
0.3%
70601528e07e2acb6666bcacf391f5512
 
0.2%
793e9ddae25d87008dd206ac57719dd22
 
0.2%
662b03fc3011541109a1f4dbb380e8ad2
 
0.2%
16c4c5d5c021757aa7828e801902c4582
 
0.2%
03390e024b0d61fa8cfe15a071cc8f242
 
0.2%
454a0aa95a860b98c62d933daafc7e1d2
 
0.2%
b4f0a3935f7048d2bec399d58ba122932
 
0.2%
0f4e558dd6742f273a3722b532836e552
 
0.2%
223f77134a78ce935272fc6660be34dd2
 
0.2%
Other values (862)896
97.7%

Most occurring characters

ValueCountFrequency (%)
01896
 
6.5%
91875
 
6.4%
21869
 
6.4%
d1862
 
6.3%
e1860
 
6.3%
71850
 
6.3%
51843
 
6.3%
81833
 
6.2%
61831
 
6.2%
b1823
 
6.2%
Other values (6)10802
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18393
62.7%
Lowercase Letter10951
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01896
10.3%
91875
10.2%
21869
10.2%
71850
10.1%
51843
10.0%
81833
10.0%
61831
10.0%
11816
9.9%
41812
9.9%
31768
9.6%
Lowercase Letter
ValueCountFrequency (%)
d1862
17.0%
e1860
17.0%
b1823
16.6%
a1811
16.5%
f1810
16.5%
c1785
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common18393
62.7%
Latin10951
37.3%

Most frequent character per script

Common
ValueCountFrequency (%)
01896
10.3%
91875
10.2%
21869
10.2%
71850
10.1%
51843
10.0%
81833
10.0%
61831
10.0%
11816
9.9%
41812
9.9%
31768
9.6%
Latin
ValueCountFrequency (%)
d1862
17.0%
e1860
17.0%
b1823
16.6%
a1811
16.5%
f1810
16.5%
c1785
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII29344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01896
 
6.5%
91875
 
6.4%
21869
 
6.4%
d1862
 
6.3%
e1860
 
6.3%
71850
 
6.3%
51843
 
6.3%
81833
 
6.2%
61831
 
6.2%
b1823
 
6.2%
Other values (6)10802
36.8%

sha1
Categorical

HIGH CARDINALITY
UNIFORM

Distinct872
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
92a91e1383f9c658d4549d3b4ec29d27a6f36038
 
3
fd44a441219aa74d4fe9db5f485c3ec005b026ee
 
2
0cb4ded45d9f3e70344d119e5435ac6b4c6433c5
 
2
44f4bdd691c8a43a4aa8234bb49fe9c07dbe768f
 
2
11efc2473da75dc0650e9b926458164d5f0209af
 
2
Other values (867)
906 

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters36680
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique828 ?
Unique (%)90.3%

Sample

1st rowfd44a441219aa74d4fe9db5f485c3ec005b026ee
2nd row4713c71b167906e4f3770eb038a7a222c46376e8
3rd row3787b187db257b1c899ddbbd2083c8487c473963
4th rowf8a7836fc8943a4554fdcf17a011d9edc81c72b7
5th row6fc797f8ebf058b79fab1d95c5c9cce613bd9d4d

Common Values

ValueCountFrequency (%)
92a91e1383f9c658d4549d3b4ec29d27a6f360383
 
0.3%
fd44a441219aa74d4fe9db5f485c3ec005b026ee2
 
0.2%
0cb4ded45d9f3e70344d119e5435ac6b4c6433c52
 
0.2%
44f4bdd691c8a43a4aa8234bb49fe9c07dbe768f2
 
0.2%
11efc2473da75dc0650e9b926458164d5f0209af2
 
0.2%
9a8b4f02ab7a559bf043c57a6f6d6c58749c550d2
 
0.2%
4f88d2c2b892de520be0de65d8549796c5db65892
 
0.2%
b1d4f3799d73f21f898a8774ada64a8d84c099f02
 
0.2%
90f81e2a66b2a69f99f32ca123f5b43c659174262
 
0.2%
15abc28447de99c64ef04ce5d781b33e1a1557892
 
0.2%
Other values (862)896
97.7%

Length

2022-09-05T11:53:30.615719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
92a91e1383f9c658d4549d3b4ec29d27a6f360383
 
0.3%
173316716bfb71df7208697806f0fc62a5d350f92
 
0.2%
8b9ed2ea45644f58501916c0f9b4b4a79e33527f2
 
0.2%
732ad07bad246872e7b653b2b1c3f3a9c2b68bbc2
 
0.2%
f2b1251ccbb76b10e3fc66cb075316c1699191e32
 
0.2%
002b59546bb340337198ac7d9dfe5da8f039be742
 
0.2%
ff399abc0d9131020189ce8db2ce20f37c26142b2
 
0.2%
4713c71b167906e4f3770eb038a7a222c46376e82
 
0.2%
e9a3f3c9157ebc1f6eb5aacb6ac5026c00d4727d2
 
0.2%
fd85812217930e30a19fa6ab612158d44b35acd32
 
0.2%
Other values (862)896
97.7%

Most occurring characters

ValueCountFrequency (%)
32375
 
6.5%
92358
 
6.4%
f2333
 
6.4%
12327
 
6.3%
02309
 
6.3%
b2303
 
6.3%
52297
 
6.3%
d2297
 
6.3%
62282
 
6.2%
22278
 
6.2%
Other values (6)13521
36.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22986
62.7%
Lowercase Letter13694
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
32375
10.3%
92358
10.3%
12327
10.1%
02309
10.0%
52297
10.0%
62282
9.9%
22278
9.9%
82261
9.8%
72254
9.8%
42245
9.8%
Lowercase Letter
ValueCountFrequency (%)
f2333
17.0%
b2303
16.8%
d2297
16.8%
a2257
16.5%
e2252
16.4%
c2252
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common22986
62.7%
Latin13694
37.3%

Most frequent character per script

Common
ValueCountFrequency (%)
32375
10.3%
92358
10.3%
12327
10.1%
02309
10.0%
52297
10.0%
62282
9.9%
22278
9.9%
82261
9.8%
72254
9.8%
42245
9.8%
Latin
ValueCountFrequency (%)
f2333
17.0%
b2303
16.8%
d2297
16.8%
a2257
16.5%
e2252
16.4%
c2252
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII36680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32375
 
6.5%
92358
 
6.4%
f2333
 
6.4%
12327
 
6.3%
02309
 
6.3%
b2303
 
6.3%
52297
 
6.3%
d2297
 
6.3%
62282
 
6.2%
22278
 
6.2%
Other values (6)13521
36.9%

sha256
Categorical

HIGH CARDINALITY
UNIFORM

Distinct872
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0ad188c147c18398d5ee2e54be5ce6a3c196ed247a352851e22e51d3a9d92c7a
 
3
3edfa8cca59d7e464270ef3b24bbefbc811cf305f4e80dbff061ec4ad7c18ea9
 
2
1fccb0fd26e47b82cb0522a39873711ea023e2dbf66e3c6e0435d267454485e7
 
2
8c212efbeaf9af762c5f4a2642c22bb0316a4089faeb6627fc411da64af9ebd8
 
2
79ef584a41008e2a42b6c48fa4a95eed727a0aa6a932d9dbf0f0d29f5c509c0b
 
2
Other values (867)
906 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters58688
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique828 ?
Unique (%)90.3%

Sample

1st row3edfa8cca59d7e464270ef3b24bbefbc811cf305f4e80dbff061ec4ad7c18ea9
2nd rowe58e0e0fe388eead6dc5d81189e556e132f388748e1a08076d977bcc68efcee7
3rd rowea2c57449ea90302b27b94e28702e8e6710196ab461c630e92c3c715930bde82
4th row41422355eb3048b27e36b590dcd690ba74b5e776099e9abdcdb56c6a665877c4
5th row6acd46ccd305db6210fae06a7eff310d1b6aab70dabd874796f312b3bee0d2b6

Common Values

ValueCountFrequency (%)
0ad188c147c18398d5ee2e54be5ce6a3c196ed247a352851e22e51d3a9d92c7a3
 
0.3%
3edfa8cca59d7e464270ef3b24bbefbc811cf305f4e80dbff061ec4ad7c18ea92
 
0.2%
1fccb0fd26e47b82cb0522a39873711ea023e2dbf66e3c6e0435d267454485e72
 
0.2%
8c212efbeaf9af762c5f4a2642c22bb0316a4089faeb6627fc411da64af9ebd82
 
0.2%
79ef584a41008e2a42b6c48fa4a95eed727a0aa6a932d9dbf0f0d29f5c509c0b2
 
0.2%
7684f8426a58c1f902e13e25c68b89a3f06a5418370a3f2aae2ba3cc3f62ebae2
 
0.2%
9d729e5168f730e49eaf26a141f912f2487b977fbfac46b2abb6372bd87f024b2
 
0.2%
aabeedd062ad05f4b5e06093c6c8ae2b197c3b712b92a1a6b80ae258f7f3b1eb2
 
0.2%
b12699ae963bb426fbdcf4b69f08caec6c32213bf6a1a7b9201c08f908c324712
 
0.2%
3f44bef1d568559c7900a705f0261ef3e539c5546f82e50e23eece917c29cc492
 
0.2%
Other values (862)896
97.7%

Length

2022-09-05T11:53:30.677941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0ad188c147c18398d5ee2e54be5ce6a3c196ed247a352851e22e51d3a9d92c7a3
 
0.3%
eea03ddc923a3c4bf8cfe8f5950d0e4ef86a33c6797fe99277e1033647af68c92
 
0.2%
e03730df898b8f3d3b080f266426afb56bdf90f4ad1e9e6b9e11f750703f075a2
 
0.2%
305afafac38ee9d132658b9569c7bc535b2b68da42ac4f0db5248b72e1a826e12
 
0.2%
872e4aa19094a5ca10204e5aeec7f5b184a508a6a547b9d232beba6aa6985ad32
 
0.2%
2b2de693c3c1041b8e391992c54c332958eb0c611aef0206ebfd94fd2a83c3122
 
0.2%
391f73c20e7bff3aa28c15a9f3cd4dae5bee26c0352af11e834434693994d55d2
 
0.2%
e58e0e0fe388eead6dc5d81189e556e132f388748e1a08076d977bcc68efcee72
 
0.2%
a0127c5d5f8788e78fbf73149e2b529bf2e7600010f51dd7b502e4228b4f67652
 
0.2%
7e9301ab6b9b3cf502ca6b5b981253f5dfdd326560b91cffc028130ae8477da02
 
0.2%
Other values (862)896
97.7%

Most occurring characters

ValueCountFrequency (%)
23842
 
6.5%
93785
 
6.4%
33723
 
6.3%
f3717
 
6.3%
03705
 
6.3%
83694
 
6.3%
53683
 
6.3%
a3657
 
6.2%
63647
 
6.2%
d3646
 
6.2%
Other values (6)21589
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36835
62.8%
Lowercase Letter21853
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
23842
10.4%
93785
10.3%
33723
10.1%
03705
10.1%
83694
10.0%
53683
10.0%
63647
9.9%
73622
9.8%
43602
9.8%
13532
9.6%
Lowercase Letter
ValueCountFrequency (%)
f3717
17.0%
a3657
16.7%
d3646
16.7%
e3620
16.6%
c3618
16.6%
b3595
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common36835
62.8%
Latin21853
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
23842
10.4%
93785
10.3%
33723
10.1%
03705
10.1%
83694
10.0%
53683
10.0%
63647
9.9%
73622
9.8%
43602
9.8%
13532
9.6%
Latin
ValueCountFrequency (%)
f3717
17.0%
a3657
16.7%
d3646
16.7%
e3620
16.6%
c3618
16.6%
b3595
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII58688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23842
 
6.5%
93785
 
6.4%
33723
 
6.3%
f3717
 
6.3%
03705
 
6.3%
83694
 
6.3%
53683
 
6.3%
a3657
 
6.2%
63647
 
6.2%
d3646
 
6.2%
Other values (6)21589
36.8%

imp_hash
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
fbcff5951ad0c204f4744c629548c6c6
917 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters29344
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfbcff5951ad0c204f4744c629548c6c6
2nd rowfbcff5951ad0c204f4744c629548c6c6
3rd rowfbcff5951ad0c204f4744c629548c6c6
4th rowfbcff5951ad0c204f4744c629548c6c6
5th rowfbcff5951ad0c204f4744c629548c6c6

Common Values

ValueCountFrequency (%)
fbcff5951ad0c204f4744c629548c6c6917
100.0%

Length

2022-09-05T11:53:30.740658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T11:53:30.804936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
fbcff5951ad0c204f4744c629548c6c6917
100.0%

Most occurring characters

ValueCountFrequency (%)
c4585
15.6%
44585
15.6%
f3668
12.5%
52751
9.4%
62751
9.4%
91834
 
6.2%
01834
 
6.2%
21834
 
6.2%
b917
 
3.1%
1917
 
3.1%
Other values (4)3668
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18340
62.5%
Lowercase Letter11004
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
44585
25.0%
52751
15.0%
62751
15.0%
91834
 
10.0%
01834
 
10.0%
21834
 
10.0%
1917
 
5.0%
7917
 
5.0%
8917
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
c4585
41.7%
f3668
33.3%
b917
 
8.3%
a917
 
8.3%
d917
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common18340
62.5%
Latin11004
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
44585
25.0%
52751
15.0%
62751
15.0%
91834
 
10.0%
01834
 
10.0%
21834
 
10.0%
1917
 
5.0%
7917
 
5.0%
8917
 
5.0%
Latin
ValueCountFrequency (%)
c4585
41.7%
f3668
33.3%
b917
 
8.3%
a917
 
8.3%
d917
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII29344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c4585
15.6%
44585
15.6%
f3668
12.5%
52751
9.4%
62751
9.4%
91834
 
6.2%
01834
 
6.2%
21834
 
6.2%
b917
 
3.1%
1917
 
3.1%
Other values (4)3668
12.5%

icon_dhash
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing917
Missing (%)100.0%
Memory size7.3 KiB

icon_raw_md5
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing917
Missing (%)100.0%
Memory size7.3 KiB

header_hash
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing917
Missing (%)100.0%
Memory size7.3 KiB

ssdeep_blocksize
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
24576
917 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4585
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24576
2nd row24576
3rd row24576
4th row24576
5th row24576

Common Values

ValueCountFrequency (%)
24576917
100.0%

Length

2022-09-05T11:53:30.858559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T11:53:30.921095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
24576917
100.0%

Most occurring characters

ValueCountFrequency (%)
2917
20.0%
4917
20.0%
5917
20.0%
7917
20.0%
6917
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4585
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2917
20.0%
4917
20.0%
5917
20.0%
7917
20.0%
6917
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common4585
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2917
20.0%
4917
20.0%
5917
20.0%
7917
20.0%
6917
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2917
20.0%
4917
20.0%
5917
20.0%
7917
20.0%
6917
20.0%

ssdeep_hash1
Categorical

HIGH CARDINALITY

Distinct194
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
NWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
 
16
gWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
 
15
tWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
 
15
OWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
 
14
fWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
 
14
Other values (189)
843 

Length

Max length38
Median length35
Mean length35.11668484
Min length35

Characters and Unicode

Total characters32202
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)6.9%

Sample

1st row2WyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
2nd rowMWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
3rd rowjWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6IjCt
4th rowrWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij
5th rowDWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij

Common Values

ValueCountFrequency (%)
NWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij16
 
1.7%
gWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij15
 
1.6%
tWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij15
 
1.6%
OWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij14
 
1.5%
fWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij14
 
1.5%
PWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij13
 
1.4%
jWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij12
 
1.3%
dWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij11
 
1.2%
hWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij11
 
1.2%
BWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6Ij11
 
1.2%
Other values (184)785
85.6%

Length

2022-09-05T11:53:31.121748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nwyohfmvmkkn3zvxehb0isaq4krico0j6ij26
 
2.8%
owyohfmvmkkn3zvxehb0isaq4krico0j6ij25
 
2.7%
pwyohfmvmkkn3zvxehb0isaq4krico0j6ij23
 
2.5%
gwyohfmvmkkn3zvxehb0isaq4krico0j6ij23
 
2.5%
twyohfmvmkkn3zvxehb0isaq4krico0j6ij22
 
2.4%
rwyohfmvmkkn3zvxehb0isaq4krico0j6ij22
 
2.4%
dwyohfmvmkkn3zvxehb0isaq4krico0j6ij20
 
2.2%
bwyohfmvmkkn3zvxehb0isaq4krico0j6ij20
 
2.2%
mwyohfmvmkkn3zvxehb0isaq4krico0j6ij20
 
2.2%
jwyohfmvmkkn3zvxehb0isaq4krico0j6ij19
 
2.1%
Other values (129)697
76.0%

Most occurring characters

ValueCountFrequency (%)
j2204
 
6.8%
K1854
 
5.8%
M1852
 
5.8%
o1850
 
5.7%
I1849
 
5.7%
01845
 
5.7%
r938
 
2.9%
C937
 
2.9%
Q937
 
2.9%
N937
 
2.9%
Other values (54)16999
52.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter13810
42.9%
Lowercase Letter13623
42.3%
Decimal Number4744
 
14.7%
Other Punctuation13
 
< 0.1%
Math Symbol12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
j2204
16.2%
o1850
13.6%
r938
6.9%
v933
6.8%
h933
6.8%
x932
6.8%
b932
6.8%
y931
6.8%
s930
6.8%
a928
6.8%
Other values (16)2112
15.5%
Uppercase Letter
ValueCountFrequency (%)
K1854
13.4%
M1852
13.4%
I1849
13.4%
C937
6.8%
Q937
6.8%
N937
6.8%
V936
6.8%
Z935
6.8%
W930
6.7%
F930
6.7%
Other values (16)1713
12.4%
Decimal Number
ValueCountFrequency (%)
01845
38.9%
6930
19.6%
4929
19.6%
3926
19.5%
137
 
0.8%
221
 
0.4%
918
 
0.4%
818
 
0.4%
712
 
0.3%
58
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/13
100.0%
Math Symbol
ValueCountFrequency (%)
+12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27433
85.2%
Common4769
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
j2204
 
8.0%
K1854
 
6.8%
M1852
 
6.8%
o1850
 
6.7%
I1849
 
6.7%
r938
 
3.4%
C937
 
3.4%
Q937
 
3.4%
N937
 
3.4%
V936
 
3.4%
Other values (42)13139
47.9%
Common
ValueCountFrequency (%)
01845
38.7%
6930
19.5%
4929
19.5%
3926
19.4%
137
 
0.8%
221
 
0.4%
918
 
0.4%
818
 
0.4%
/13
 
0.3%
+12
 
0.3%
Other values (2)20
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII32202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
j2204
 
6.8%
K1854
 
5.8%
M1852
 
5.8%
o1850
 
5.7%
I1849
 
5.7%
01845
 
5.7%
r938
 
2.9%
C937
 
2.9%
Q937
 
2.9%
N937
 
2.9%
Other values (54)16999
52.8%

ssdeep_hash2
Categorical

HIGH CARDINALITY

Distinct140
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
3nuVMK6vx2RsIKNrj
 
19
4nuVMK6vx2RsIKNrj
 
16
YnuVMK6vx2RsIKNrj
 
16
SnuVMK6vx2RsIKNrj
 
14
unuVMK6vx2RsIKNrj
 
14
Other values (135)
838 

Length

Max length19
Median length17
Mean length17.0087241
Min length17

Characters and Unicode

Total characters15597
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.5%

Sample

1st rowJnuVMK6vx2RsIKNrj
2nd rowTnuVMK6vx2RsIKNrj
3rd rowSnuVMK6vx2RsIKNrj
4th rowanuVMK6vx2RsIKNrj
5th rowynuVMK6vx2RsIKNrj

Common Values

ValueCountFrequency (%)
3nuVMK6vx2RsIKNrj19
 
2.1%
4nuVMK6vx2RsIKNrj16
 
1.7%
YnuVMK6vx2RsIKNrj16
 
1.7%
SnuVMK6vx2RsIKNrj14
 
1.5%
unuVMK6vx2RsIKNrj14
 
1.5%
BnuVMK6vx2RsIKNrj14
 
1.5%
lnuVMK6vx2RsIKNrj13
 
1.4%
+nuVMK6vx2RsIKNrj13
 
1.4%
cnuVMK6vx2RsIKNrj13
 
1.4%
/nuVMK6vx2RsIKNrj12
 
1.3%
Other values (130)773
84.3%

Length

2022-09-05T11:53:31.198738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ynuvmk6vx2rsiknrj26
 
2.8%
nuvmk6vx2rsiknrj25
 
2.7%
unuvmk6vx2rsiknrj23
 
2.5%
enuvmk6vx2rsiknrj22
 
2.4%
snuvmk6vx2rsiknrj22
 
2.4%
cnuvmk6vx2rsiknrj22
 
2.4%
wnuvmk6vx2rsiknrj22
 
2.4%
tnuvmk6vx2rsiknrj21
 
2.3%
lnuvmk6vx2rsiknrj21
 
2.3%
nnuvmk6vx2rsiknrj21
 
2.3%
Other values (77)692
75.5%

Most occurring characters

ValueCountFrequency (%)
K1845
 
11.8%
j937
 
6.0%
u936
 
6.0%
N935
 
6.0%
M934
 
6.0%
I934
 
6.0%
2932
 
6.0%
s932
 
6.0%
x930
 
6.0%
R928
 
5.9%
Other values (54)5354
34.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7145
45.8%
Lowercase Letter6445
41.3%
Decimal Number1977
 
12.7%
Math Symbol16
 
0.1%
Other Punctuation14
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K1845
25.8%
N935
13.1%
M934
13.1%
I934
13.1%
R928
13.0%
V927
13.0%
D357
 
5.0%
Y20
 
0.3%
S20
 
0.3%
E19
 
0.3%
Other values (16)226
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
j937
14.5%
u936
14.5%
s932
14.5%
x930
14.4%
v926
14.4%
r925
14.4%
n578
9.0%
y20
 
0.3%
w19
 
0.3%
a19
 
0.3%
Other values (16)223
 
3.5%
Decimal Number
ValueCountFrequency (%)
2932
47.1%
6925
46.8%
324
 
1.2%
423
 
1.2%
815
 
0.8%
514
 
0.7%
113
 
0.7%
912
 
0.6%
711
 
0.6%
08
 
0.4%
Math Symbol
ValueCountFrequency (%)
+16
100.0%
Other Punctuation
ValueCountFrequency (%)
/14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13590
87.1%
Common2007
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
K1845
13.6%
j937
 
6.9%
u936
 
6.9%
N935
 
6.9%
M934
 
6.9%
I934
 
6.9%
s932
 
6.9%
x930
 
6.8%
R928
 
6.8%
V927
 
6.8%
Other values (42)3352
24.7%
Common
ValueCountFrequency (%)
2932
46.4%
6925
46.1%
324
 
1.2%
423
 
1.1%
+16
 
0.8%
815
 
0.7%
514
 
0.7%
/14
 
0.7%
113
 
0.6%
912
 
0.6%
Other values (2)19
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII15597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K1845
 
11.8%
j937
 
6.0%
u936
 
6.0%
N935
 
6.0%
M934
 
6.0%
I934
 
6.0%
2932
 
6.0%
s932
 
6.0%
x930
 
6.0%
R928
 
5.9%
Other values (54)5354
34.3%

tlsh
Categorical

HIGH CARDINALITY
UNIFORM

Distinct786
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
T1FD45D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C93
 
4
T12E55D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C93
 
4
T17F55D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C93
 
3
T1CA55D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C93
 
3
T16145D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C93
 
3
Other values (781)
900 

Length

Max length72
Median length72
Mean length72
Min length72

Characters and Unicode

Total characters66024
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique679 ?
Unique (%)74.0%

Sample

1st rowT1B415D011F5F630E6CC662E3CAE480B1E5022F05DA315279B179E5390ADACA7B5EF4C93
2nd rowT1D755D011F5F630E6CC262E3CAE480B1E5022F05DA315279B179E5390ADACA7B5EF4C93
3rd rowT1CB75D011F5F630E6CC662E3CAE480B1E5022F05DA315279B179E5390ADACA7B5EF4C93
4th rowT18055D021F5F630E6CC662E3CAE480B5E5022F05DA315279B175E4390ADACA7B5EF4C93
5th rowT141657C11F3F310D3C89EFA3CBD9D0919B053E259851F471E064F1361AC9ABA32EA9997

Common Values

ValueCountFrequency (%)
T1FD45D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C934
 
0.4%
T12E55D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C934
 
0.4%
T17F55D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
T1CA55D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
T16145D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
T19F55D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
T19A45D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
T1B755D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
T1CA45D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
T13255D011F5F630E6CC662E3CAE480B5E5022F05DA315279B179E4390ADACA7B5EF4C933
 
0.3%
Other values (776)885
96.5%

Length

2022-09-05T11:53:31.268770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
t1fd45d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c934
 
0.4%
t12e55d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c934
 
0.4%
t19f45d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c933
 
0.3%
t1bf55d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c933
 
0.3%
t10f45d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c933
 
0.3%
t1b355d011f5f630e6cc262e3cae480b5e5022f059a315279b179f5390adaca7b5ef4c933
 
0.3%
t1d155d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c933
 
0.3%
t1fa55d011f5f630e6cc662e3cae480b1e5022f05da315279b179e5390adaca7b5ef4c933
 
0.3%
t12345d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c933
 
0.3%
t15645d011f5f630e6cc662e3cae480b5e5022f05da315279b179e4390adaca7b5ef4c933
 
0.3%
Other values (776)885
96.5%

Most occurring characters

ValueCountFrequency (%)
56815
 
10.3%
E5507
 
8.3%
05420
 
8.2%
15252
 
8.0%
A4779
 
7.2%
34711
 
7.1%
C4575
 
6.9%
24026
 
6.1%
F3911
 
5.9%
93785
 
5.7%
Other values (7)17243
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40717
61.7%
Uppercase Letter25307
38.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
56815
16.7%
05420
13.3%
15252
12.9%
34711
11.6%
24026
9.9%
93785
9.3%
63636
8.9%
73092
7.6%
42891
7.1%
81089
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
E5507
21.8%
A4779
18.9%
C4575
18.1%
F3911
15.5%
B2834
11.2%
D2784
11.0%
T917
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common40717
61.7%
Latin25307
38.3%

Most frequent character per script

Common
ValueCountFrequency (%)
56815
16.7%
05420
13.3%
15252
12.9%
34711
11.6%
24026
9.9%
93785
9.3%
63636
8.9%
73092
7.6%
42891
7.1%
81089
 
2.7%
Latin
ValueCountFrequency (%)
E5507
21.8%
A4779
18.9%
C4575
18.1%
F3911
15.5%
B2834
11.2%
D2784
11.0%
T917
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII66024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56815
 
10.3%
E5507
 
8.3%
05420
 
8.2%
15252
 
8.0%
A4779
 
7.2%
34711
 
7.1%
C4575
 
6.9%
24026
 
6.1%
F3911
 
5.9%
93785
 
5.7%
Other values (7)17243
26.1%

vhash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct612
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
1164f6651d751d7515550055z2001c079z11z1011z1121z29zbb
 
8
1164f6651d751d7515550055z2001c079z11z1011z1121z28z11z
 
7
1164a6651d751d7515550055z2001c079z11z1011z1121z2az6
 
6
1164f6651d751d7515550055z2001c079z11z1011z1121z29z45
 
6
1164d6651d751d7515550055z2001c079z11z1011z1121z2az6
 
4
Other values (607)
886 

Length

Max length53
Median length52
Mean length51.89858233
Min length51

Characters and Unicode

Total characters47591
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)44.1%

Sample

1st row185146651d751d7515550055z2001c079z11z1011z1121z29zb5
2nd row116306651d751d7515550055z2001c079z11z1011z1121z29zb5
3rd row1163d6651d751d7515550055z2001c079z11z1011z1121z29z2a
4th row116356651d751d7515550055z2001c079z11z1011z1121z29z66
5th row116256651d751d7515550055z2001c079z11z1011z1121z28zcbd

Common Values

ValueCountFrequency (%)
1164f6651d751d7515550055z2001c079z11z1011z1121z29zbb8
 
0.9%
1164f6651d751d7515550055z2001c079z11z1011z1121z28z11z7
 
0.8%
1164a6651d751d7515550055z2001c079z11z1011z1121z2az66
 
0.7%
1164f6651d751d7515550055z2001c079z11z1011z1121z29z456
 
0.7%
1164d6651d751d7515550055z2001c079z11z1011z1121z2az64
 
0.4%
116456651d751d7515550055z2001c079z11z1011z1121z29z114
 
0.4%
1164f6651d751d7515550055z2001c079z11z1011z1121z2az64
 
0.4%
1164f6651d751d7515550055z2001c079z11z1011z1121z29z114
 
0.4%
116466651d751d7515550055z2001c079z11z1011z1121z29z1d4
 
0.4%
1164c6651d751d7515550055z2001c079z11z1011z1121z29zbb4
 
0.4%
Other values (602)866
94.4%

Length

2022-09-05T11:53:31.337391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1164f6651d751d7515550055z2001c079z11z1011z1121z29zbb8
 
0.9%
1164f6651d751d7515550055z2001c079z11z1011z1121z28z11z7
 
0.8%
1164a6651d751d7515550055z2001c079z11z1011z1121z2az66
 
0.7%
1164f6651d751d7515550055z2001c079z11z1011z1121z29z456
 
0.7%
1164d6651d751d7515550055z2001c079z11z1011z1121z28z11z4
 
0.4%
1164d6651d751d7515550055z2001c079z11z1011z1121z29z1d4
 
0.4%
1164e6651d751d7515550055z2001c079z11z1011z1121z28z11z4
 
0.4%
1164d6651d751d7515550055z2001c079z11z1011z1121z29z284
 
0.4%
1164c6651d751d7515550055z2001c079z11z1011z1121z29z1d4
 
0.4%
1164a6651d751d7515550055z2001c079z11z1011z1121z29z1c4
 
0.4%
Other values (602)866
94.4%

Most occurring characters

ValueCountFrequency (%)
113274
27.9%
57630
16.0%
z5555
11.7%
05553
11.7%
23021
 
6.3%
62942
 
6.2%
72855
 
6.0%
d1999
 
4.2%
91670
 
3.5%
c1058
 
2.2%
Other values (7)2034
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number38062
80.0%
Lowercase Letter9529
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
113274
34.9%
57630
20.0%
05553
14.6%
23021
 
7.9%
62942
 
7.7%
72855
 
7.5%
91670
 
4.4%
4580
 
1.5%
3314
 
0.8%
8223
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
z5555
58.3%
d1999
 
21.0%
c1058
 
11.1%
a399
 
4.2%
b237
 
2.5%
f162
 
1.7%
e119
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common38062
80.0%
Latin9529
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
113274
34.9%
57630
20.0%
05553
14.6%
23021
 
7.9%
62942
 
7.7%
72855
 
7.5%
91670
 
4.4%
4580
 
1.5%
3314
 
0.8%
8223
 
0.6%
Latin
ValueCountFrequency (%)
z5555
58.3%
d1999
 
21.0%
c1058
 
11.1%
a399
 
4.2%
b237
 
2.5%
f162
 
1.7%
e119
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII47591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113274
27.9%
57630
16.0%
z5555
11.7%
05553
11.7%
23021
 
6.3%
62942
 
6.2%
72855
 
6.0%
d1999
 
4.2%
91670
 
3.5%
c1058
 
2.2%
Other values (7)2034
 
4.3%

Interactions

2022-09-05T11:53:27.751311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.281549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.861473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.335946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.820293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.289125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.832982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.370339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.939633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.416205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.899248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.366542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.913376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.448426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.018946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.497080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.976970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.442358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.997123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.623443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.099177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.580326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.058105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.519724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:28.075574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.701398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.177913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.659097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.134640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.595897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:28.155124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:25.778427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.254130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:26.737861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.208466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-05T11:53:27.670412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-05T11:53:31.420138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-05T11:53:31.535046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-05T11:53:31.647576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-05T11:53:31.752889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-05T11:53:31.827785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-05T11:53:28.410676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-05T11:53:28.685444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-05T11:53:28.792688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexUnnamed: 0filenamewin_countauthentihashfiletypecodesizetimestampmaliciousundetectedresources_lensections_lenfile_md5sha1sha256imp_hashicon_dhashicon_raw_md5header_hashssdeep_blocksizessdeep_hash1ssdeep_hash2tlshvhash
099099020220329/2022032900/2022032900_1099132fa4c1fbc826879408746ffb81874bac8170c6db1e305f216c2b8b793b3eda0Win64 DLL3276820204817120a5aca04c37505d443e527b377c1f5355fd44a441219aa74d4fe9db5f485c3ec005b026ee3edfa8cca59d7e464270ef3b24bbefbc811cf305f4e80dbff061ec4ad7c18ea9fbcff5951ad0c204f4744c629548c6c6NaNNaNNaN245762WyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6IjJnuVMK6vx2RsIKNrjT1B415D011F5F630E6CC662E3CAE480B1E5022F05DA315279B179E5390ADACA7B5EF4C93185146651d751d7515550055z2001c079z11z1011z1121z29zb5
11607160720220329/2022032900/2022032900_111608208f9cdedc66cb3f91e2d654f0f5a1f7957434bb919f76e53ad95095532b7d7dWin64 DLL3276820204918148b4f0a3935f7048d2bec399d58ba122934713c71b167906e4f3770eb038a7a222c46376e8e58e0e0fe388eead6dc5d81189e556e132f388748e1a08076d977bcc68efcee7fbcff5951ad0c204f4744c629548c6c6NaNNaNNaN24576MWyoHFMVMKkN3ZvxEhb0IsaQ4KriCo0j6IjTnuVMK6vx2RsIKNrjT1D755D011F5F630E6CC262E3CAE480B1E5022F05DA315279B179E5390ADACA7B5EF4C93116306651d751d7515550055z2001c079z11z1011z1121z29zb5
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Last rows

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